{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "92c9da5866bdcf7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T04:42:53.841767Z",
     "start_time": "2025-06-30T04:42:52.883997Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'漫', '少', '江', '茫', '山', '水', '；', '看', '百', '游', '舸', '华', '舟', '\\n', '洲', '碧', '自', '争', '？', '染', '主', '挥', '遍', '侯', '橘', '子', '记', '往', '风', '斥', '尽', '书', '底', '苍', '侣', '地', '户', '问', '万', '扬', '字', '气', '否', '谁', '中', '浅', '激', '鱼', '类', '岁', '到', '去', '由', '竞', '飞', '湘', '茂', '浪', '翔', '层', '意', '方', '怅', '霜', '红', '独', '年', '流', '立', '北', '头', '沉', '土', '粪', '廓', '携', '忆', '正', '点', '峥', '秋', '浮', '指', '大', '击', '嵘', '林', '来', '生', '。', '天', '寥', '寒', '透', '遒', '同', '长', '当', '遏', '鹰', '曾', '稠', '空', '文', '昔', '，', '恰', '月', '学'}\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "import numpy as np\n",
    "\n",
    "# 示例文本数据，一首诗\n",
    "text = \"\"\"\n",
    "独立寒秋，湘江北去，橘子洲头。\n",
    "看万山红遍，层林尽染；漫江碧透，百舸争流。\n",
    "鹰击长空，鱼翔浅底，万类霜天竞自由。\n",
    "怅寥廓，问苍茫大地，谁主沉浮？\n",
    "携来百侣曾游，忆往昔峥嵘岁月稠。\n",
    "恰同学少年，风华正茂；书生意气，挥斥方遒。\n",
    "指点江山，激扬文字，粪土当年万户侯。\n",
    "曾记否，到中流击水，浪遏飞舟？\n",
    "\"\"\"\n",
    "\n",
    "# 创建词汇表\n",
    "words = set(text)\n",
    "vocab_size = len(words)\n",
    "word_to_idx = {word: i for i, word in enumerate(words)}\n",
    "idx_to_word = {i: word for i, word in enumerate(words)}\n",
    "\n",
    "print(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "91280acf83012c57",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T04:43:06.668624Z",
     "start_time": "2025-06-30T04:43:06.664832Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13, 65, 68, 92, 80, 105, 55, 2, 69, 51, 105, 24, 25, 14, 70, 89, 13, 7, 38, 4, 64, 22, 105, 59, 86, 30, 19, 6, 0, 2, 15, 93, 105, 8, 10, 17, 67, 89, 13, 99, 84, 96, 102, 105, 47, 58, 45, 32, 105, 38, 48, 63, 90, 53, 16, 52, 89, 13, 62, 91, 74, 105, 37, 33, 3, 83, 35, 105, 43, 20, 71, 81, 18, 13, 75, 87, 8, 34, 100, 9, 105, 76, 27, 104, 79, 85, 49, 107, 101, 89, 13, 106, 95, 108, 1, 66, 105, 28, 11, 77, 56, 6, 31, 88, 60, 41, 105, 21, 29, 61, 94, 89, 13, 82, 78, 2, 4, 105, 46, 39, 103, 40, 105, 73, 72, 97, 66, 38, 36, 23, 89, 13, 100, 26, 42, 105, 50, 44, 67, 84, 5, 105, 57, 98, 54, 12, 18, 13]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# 超参数设置\n",
    "SEQ_LENGTH = 10  # 输入序列长度\n",
    "BATCH_SIZE = 1\n",
    "HIDDEN_SIZE = 128\n",
    "\n",
    "\n",
    "# 创建训练数据\n",
    "class TextDataset(Dataset):\n",
    "    def __init__(self, text, seq_length):\n",
    "        self.text = text\n",
    "        self.seq_length = seq_length\n",
    "\n",
    "        # 转换为索引序列\n",
    "        self.data = [word_to_idx[ch] for ch in text]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data) - self.seq_length\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        # 文本里的某个序列\n",
    "        input_seq = self.data[idx:idx + self.seq_length]\n",
    "\n",
    "        # 目标序列\n",
    "        target_seq = self.data[idx + 1:idx + self.seq_length + 1]\n",
    "\n",
    "        # 相当于，假如语料为abcdefg, input_seq=abc, target_seq=bcd\n",
    "\n",
    "        return torch.LongTensor(input_seq), torch.LongTensor(target_seq)\n",
    "\n",
    "\n",
    "dataset = TextDataset(text, SEQ_LENGTH)\n",
    "dataloader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=False)\n",
    "print(dataset.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "88cba6d2660e76b2",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T04:43:09.190044Z",
     "start_time": "2025-06-30T04:43:09.159925Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 13,  65,  68,  92,  80, 105,  55,   2,  69,  51]])\n",
      "tensor([[ 65,  68,  92,  80, 105,  55,   2,  69,  51, 105]])\n",
      "tensor([[ 65,  68,  92,  80, 105,  55,   2,  69,  51, 105]])\n",
      "tensor([[ 68,  92,  80, 105,  55,   2,  69,  51, 105,  24]])\n",
      "tensor([[ 68,  92,  80, 105,  55,   2,  69,  51, 105,  24]])\n",
      "tensor([[ 92,  80, 105,  55,   2,  69,  51, 105,  24,  25]])\n",
      "tensor([[ 92,  80, 105,  55,   2,  69,  51, 105,  24,  25]])\n",
      "tensor([[ 80, 105,  55,   2,  69,  51, 105,  24,  25,  14]])\n",
      "tensor([[ 80, 105,  55,   2,  69,  51, 105,  24,  25,  14]])\n",
      "tensor([[105,  55,   2,  69,  51, 105,  24,  25,  14,  70]])\n",
      "tensor([[105,  55,   2,  69,  51, 105,  24,  25,  14,  70]])\n",
      "tensor([[ 55,   2,  69,  51, 105,  24,  25,  14,  70,  89]])\n",
      "tensor([[ 55,   2,  69,  51, 105,  24,  25,  14,  70,  89]])\n",
      "tensor([[  2,  69,  51, 105,  24,  25,  14,  70,  89,  13]])\n",
      "tensor([[  2,  69,  51, 105,  24,  25,  14,  70,  89,  13]])\n",
      "tensor([[ 69,  51, 105,  24,  25,  14,  70,  89,  13,   7]])\n",
      "tensor([[ 69,  51, 105,  24,  25,  14,  70,  89,  13,   7]])\n",
      "tensor([[ 51, 105,  24,  25,  14,  70,  89,  13,   7,  38]])\n",
      "tensor([[ 51, 105,  24,  25,  14,  70,  89,  13,   7,  38]])\n",
      "tensor([[105,  24,  25,  14,  70,  89,  13,   7,  38,   4]])\n",
      "tensor([[105,  24,  25,  14,  70,  89,  13,   7,  38,   4]])\n",
      "tensor([[24, 25, 14, 70, 89, 13,  7, 38,  4, 64]])\n",
      "tensor([[24, 25, 14, 70, 89, 13,  7, 38,  4, 64]])\n",
      "tensor([[25, 14, 70, 89, 13,  7, 38,  4, 64, 22]])\n",
      "tensor([[25, 14, 70, 89, 13,  7, 38,  4, 64, 22]])\n",
      "tensor([[ 14,  70,  89,  13,   7,  38,   4,  64,  22, 105]])\n",
      "tensor([[ 14,  70,  89,  13,   7,  38,   4,  64,  22, 105]])\n",
      "tensor([[ 70,  89,  13,   7,  38,   4,  64,  22, 105,  59]])\n",
      "tensor([[ 70,  89,  13,   7,  38,   4,  64,  22, 105,  59]])\n",
      "tensor([[ 89,  13,   7,  38,   4,  64,  22, 105,  59,  86]])\n",
      "tensor([[ 89,  13,   7,  38,   4,  64,  22, 105,  59,  86]])\n",
      "tensor([[ 13,   7,  38,   4,  64,  22, 105,  59,  86,  30]])\n",
      "tensor([[ 13,   7,  38,   4,  64,  22, 105,  59,  86,  30]])\n",
      "tensor([[  7,  38,   4,  64,  22, 105,  59,  86,  30,  19]])\n",
      "tensor([[  7,  38,   4,  64,  22, 105,  59,  86,  30,  19]])\n",
      "tensor([[ 38,   4,  64,  22, 105,  59,  86,  30,  19,   6]])\n",
      "tensor([[ 38,   4,  64,  22, 105,  59,  86,  30,  19,   6]])\n",
      "tensor([[  4,  64,  22, 105,  59,  86,  30,  19,   6,   0]])\n",
      "tensor([[  4,  64,  22, 105,  59,  86,  30,  19,   6,   0]])\n",
      "tensor([[ 64,  22, 105,  59,  86,  30,  19,   6,   0,   2]])\n",
      "tensor([[ 64,  22, 105,  59,  86,  30,  19,   6,   0,   2]])\n",
      "tensor([[ 22, 105,  59,  86,  30,  19,   6,   0,   2,  15]])\n",
      "tensor([[ 22, 105,  59,  86,  30,  19,   6,   0,   2,  15]])\n",
      "tensor([[105,  59,  86,  30,  19,   6,   0,   2,  15,  93]])\n",
      "tensor([[105,  59,  86,  30,  19,   6,   0,   2,  15,  93]])\n",
      "tensor([[ 59,  86,  30,  19,   6,   0,   2,  15,  93, 105]])\n",
      "tensor([[ 59,  86,  30,  19,   6,   0,   2,  15,  93, 105]])\n",
      "tensor([[ 86,  30,  19,   6,   0,   2,  15,  93, 105,   8]])\n",
      "tensor([[ 86,  30,  19,   6,   0,   2,  15,  93, 105,   8]])\n",
      "tensor([[ 30,  19,   6,   0,   2,  15,  93, 105,   8,  10]])\n",
      "tensor([[ 30,  19,   6,   0,   2,  15,  93, 105,   8,  10]])\n",
      "tensor([[ 19,   6,   0,   2,  15,  93, 105,   8,  10,  17]])\n",
      "tensor([[ 19,   6,   0,   2,  15,  93, 105,   8,  10,  17]])\n",
      "tensor([[  6,   0,   2,  15,  93, 105,   8,  10,  17,  67]])\n",
      "tensor([[  6,   0,   2,  15,  93, 105,   8,  10,  17,  67]])\n",
      "tensor([[  0,   2,  15,  93, 105,   8,  10,  17,  67,  89]])\n",
      "tensor([[  0,   2,  15,  93, 105,   8,  10,  17,  67,  89]])\n",
      "tensor([[  2,  15,  93, 105,   8,  10,  17,  67,  89,  13]])\n",
      "tensor([[  2,  15,  93, 105,   8,  10,  17,  67,  89,  13]])\n",
      "tensor([[ 15,  93, 105,   8,  10,  17,  67,  89,  13,  99]])\n",
      "tensor([[ 15,  93, 105,   8,  10,  17,  67,  89,  13,  99]])\n",
      "tensor([[ 93, 105,   8,  10,  17,  67,  89,  13,  99,  84]])\n",
      "tensor([[ 93, 105,   8,  10,  17,  67,  89,  13,  99,  84]])\n",
      "tensor([[105,   8,  10,  17,  67,  89,  13,  99,  84,  96]])\n",
      "tensor([[105,   8,  10,  17,  67,  89,  13,  99,  84,  96]])\n",
      "tensor([[  8,  10,  17,  67,  89,  13,  99,  84,  96, 102]])\n",
      "tensor([[  8,  10,  17,  67,  89,  13,  99,  84,  96, 102]])\n",
      "tensor([[ 10,  17,  67,  89,  13,  99,  84,  96, 102, 105]])\n",
      "tensor([[ 10,  17,  67,  89,  13,  99,  84,  96, 102, 105]])\n",
      "tensor([[ 17,  67,  89,  13,  99,  84,  96, 102, 105,  47]])\n",
      "tensor([[ 17,  67,  89,  13,  99,  84,  96, 102, 105,  47]])\n",
      "tensor([[ 67,  89,  13,  99,  84,  96, 102, 105,  47,  58]])\n",
      "tensor([[ 67,  89,  13,  99,  84,  96, 102, 105,  47,  58]])\n",
      "tensor([[ 89,  13,  99,  84,  96, 102, 105,  47,  58,  45]])\n",
      "tensor([[ 89,  13,  99,  84,  96, 102, 105,  47,  58,  45]])\n",
      "tensor([[ 13,  99,  84,  96, 102, 105,  47,  58,  45,  32]])\n",
      "tensor([[ 13,  99,  84,  96, 102, 105,  47,  58,  45,  32]])\n",
      "tensor([[ 99,  84,  96, 102, 105,  47,  58,  45,  32, 105]])\n",
      "tensor([[ 99,  84,  96, 102, 105,  47,  58,  45,  32, 105]])\n",
      "tensor([[ 84,  96, 102, 105,  47,  58,  45,  32, 105,  38]])\n",
      "tensor([[ 84,  96, 102, 105,  47,  58,  45,  32, 105,  38]])\n",
      "tensor([[ 96, 102, 105,  47,  58,  45,  32, 105,  38,  48]])\n",
      "tensor([[ 96, 102, 105,  47,  58,  45,  32, 105,  38,  48]])\n",
      "tensor([[102, 105,  47,  58,  45,  32, 105,  38,  48,  63]])\n",
      "tensor([[102, 105,  47,  58,  45,  32, 105,  38,  48,  63]])\n",
      "tensor([[105,  47,  58,  45,  32, 105,  38,  48,  63,  90]])\n",
      "tensor([[105,  47,  58,  45,  32, 105,  38,  48,  63,  90]])\n",
      "tensor([[ 47,  58,  45,  32, 105,  38,  48,  63,  90,  53]])\n",
      "tensor([[ 47,  58,  45,  32, 105,  38,  48,  63,  90,  53]])\n",
      "tensor([[ 58,  45,  32, 105,  38,  48,  63,  90,  53,  16]])\n",
      "tensor([[ 58,  45,  32, 105,  38,  48,  63,  90,  53,  16]])\n",
      "tensor([[ 45,  32, 105,  38,  48,  63,  90,  53,  16,  52]])\n",
      "tensor([[ 45,  32, 105,  38,  48,  63,  90,  53,  16,  52]])\n",
      "tensor([[ 32, 105,  38,  48,  63,  90,  53,  16,  52,  89]])\n",
      "tensor([[ 32, 105,  38,  48,  63,  90,  53,  16,  52,  89]])\n",
      "tensor([[105,  38,  48,  63,  90,  53,  16,  52,  89,  13]])\n",
      "tensor([[105,  38,  48,  63,  90,  53,  16,  52,  89,  13]])\n",
      "tensor([[38, 48, 63, 90, 53, 16, 52, 89, 13, 62]])\n",
      "tensor([[38, 48, 63, 90, 53, 16, 52, 89, 13, 62]])\n",
      "tensor([[48, 63, 90, 53, 16, 52, 89, 13, 62, 91]])\n",
      "tensor([[48, 63, 90, 53, 16, 52, 89, 13, 62, 91]])\n",
      "tensor([[63, 90, 53, 16, 52, 89, 13, 62, 91, 74]])\n",
      "tensor([[63, 90, 53, 16, 52, 89, 13, 62, 91, 74]])\n",
      "tensor([[ 90,  53,  16,  52,  89,  13,  62,  91,  74, 105]])\n",
      "tensor([[ 90,  53,  16,  52,  89,  13,  62,  91,  74, 105]])\n",
      "tensor([[ 53,  16,  52,  89,  13,  62,  91,  74, 105,  37]])\n",
      "tensor([[ 53,  16,  52,  89,  13,  62,  91,  74, 105,  37]])\n",
      "tensor([[ 16,  52,  89,  13,  62,  91,  74, 105,  37,  33]])\n",
      "tensor([[ 16,  52,  89,  13,  62,  91,  74, 105,  37,  33]])\n",
      "tensor([[ 52,  89,  13,  62,  91,  74, 105,  37,  33,   3]])\n",
      "tensor([[ 52,  89,  13,  62,  91,  74, 105,  37,  33,   3]])\n",
      "tensor([[ 89,  13,  62,  91,  74, 105,  37,  33,   3,  83]])\n",
      "tensor([[ 89,  13,  62,  91,  74, 105,  37,  33,   3,  83]])\n",
      "tensor([[ 13,  62,  91,  74, 105,  37,  33,   3,  83,  35]])\n",
      "tensor([[ 13,  62,  91,  74, 105,  37,  33,   3,  83,  35]])\n",
      "tensor([[ 62,  91,  74, 105,  37,  33,   3,  83,  35, 105]])\n",
      "tensor([[ 62,  91,  74, 105,  37,  33,   3,  83,  35, 105]])\n",
      "tensor([[ 91,  74, 105,  37,  33,   3,  83,  35, 105,  43]])\n",
      "tensor([[ 91,  74, 105,  37,  33,   3,  83,  35, 105,  43]])\n",
      "tensor([[ 74, 105,  37,  33,   3,  83,  35, 105,  43,  20]])\n",
      "tensor([[ 74, 105,  37,  33,   3,  83,  35, 105,  43,  20]])\n",
      "tensor([[105,  37,  33,   3,  83,  35, 105,  43,  20,  71]])\n",
      "tensor([[105,  37,  33,   3,  83,  35, 105,  43,  20,  71]])\n",
      "tensor([[ 37,  33,   3,  83,  35, 105,  43,  20,  71,  81]])\n",
      "tensor([[ 37,  33,   3,  83,  35, 105,  43,  20,  71,  81]])\n",
      "tensor([[ 33,   3,  83,  35, 105,  43,  20,  71,  81,  18]])\n",
      "tensor([[ 33,   3,  83,  35, 105,  43,  20,  71,  81,  18]])\n",
      "tensor([[  3,  83,  35, 105,  43,  20,  71,  81,  18,  13]])\n",
      "tensor([[  3,  83,  35, 105,  43,  20,  71,  81,  18,  13]])\n",
      "tensor([[ 83,  35, 105,  43,  20,  71,  81,  18,  13,  75]])\n",
      "tensor([[ 83,  35, 105,  43,  20,  71,  81,  18,  13,  75]])\n",
      "tensor([[ 35, 105,  43,  20,  71,  81,  18,  13,  75,  87]])\n",
      "tensor([[ 35, 105,  43,  20,  71,  81,  18,  13,  75,  87]])\n",
      "tensor([[105,  43,  20,  71,  81,  18,  13,  75,  87,   8]])\n",
      "tensor([[105,  43,  20,  71,  81,  18,  13,  75,  87,   8]])\n",
      "tensor([[43, 20, 71, 81, 18, 13, 75, 87,  8, 34]])\n",
      "tensor([[43, 20, 71, 81, 18, 13, 75, 87,  8, 34]])\n",
      "tensor([[ 20,  71,  81,  18,  13,  75,  87,   8,  34, 100]])\n",
      "tensor([[ 20,  71,  81,  18,  13,  75,  87,   8,  34, 100]])\n",
      "tensor([[ 71,  81,  18,  13,  75,  87,   8,  34, 100,   9]])\n",
      "tensor([[ 71,  81,  18,  13,  75,  87,   8,  34, 100,   9]])\n",
      "tensor([[ 81,  18,  13,  75,  87,   8,  34, 100,   9, 105]])\n",
      "tensor([[ 81,  18,  13,  75,  87,   8,  34, 100,   9, 105]])\n",
      "tensor([[ 18,  13,  75,  87,   8,  34, 100,   9, 105,  76]])\n",
      "tensor([[ 18,  13,  75,  87,   8,  34, 100,   9, 105,  76]])\n",
      "tensor([[ 13,  75,  87,   8,  34, 100,   9, 105,  76,  27]])\n",
      "tensor([[ 13,  75,  87,   8,  34, 100,   9, 105,  76,  27]])\n",
      "tensor([[ 75,  87,   8,  34, 100,   9, 105,  76,  27, 104]])\n",
      "tensor([[ 75,  87,   8,  34, 100,   9, 105,  76,  27, 104]])\n",
      "tensor([[ 87,   8,  34, 100,   9, 105,  76,  27, 104,  79]])\n",
      "tensor([[ 87,   8,  34, 100,   9, 105,  76,  27, 104,  79]])\n",
      "tensor([[  8,  34, 100,   9, 105,  76,  27, 104,  79,  85]])\n",
      "tensor([[  8,  34, 100,   9, 105,  76,  27, 104,  79,  85]])\n",
      "tensor([[ 34, 100,   9, 105,  76,  27, 104,  79,  85,  49]])\n",
      "tensor([[ 34, 100,   9, 105,  76,  27, 104,  79,  85,  49]])\n",
      "tensor([[100,   9, 105,  76,  27, 104,  79,  85,  49, 107]])\n",
      "tensor([[100,   9, 105,  76,  27, 104,  79,  85,  49, 107]])\n",
      "tensor([[  9, 105,  76,  27, 104,  79,  85,  49, 107, 101]])\n",
      "tensor([[  9, 105,  76,  27, 104,  79,  85,  49, 107, 101]])\n",
      "tensor([[105,  76,  27, 104,  79,  85,  49, 107, 101,  89]])\n",
      "tensor([[105,  76,  27, 104,  79,  85,  49, 107, 101,  89]])\n",
      "tensor([[ 76,  27, 104,  79,  85,  49, 107, 101,  89,  13]])\n",
      "tensor([[ 76,  27, 104,  79,  85,  49, 107, 101,  89,  13]])\n",
      "tensor([[ 27, 104,  79,  85,  49, 107, 101,  89,  13, 106]])\n",
      "tensor([[ 27, 104,  79,  85,  49, 107, 101,  89,  13, 106]])\n",
      "tensor([[104,  79,  85,  49, 107, 101,  89,  13, 106,  95]])\n",
      "tensor([[104,  79,  85,  49, 107, 101,  89,  13, 106,  95]])\n",
      "tensor([[ 79,  85,  49, 107, 101,  89,  13, 106,  95, 108]])\n",
      "tensor([[ 79,  85,  49, 107, 101,  89,  13, 106,  95, 108]])\n",
      "tensor([[ 85,  49, 107, 101,  89,  13, 106,  95, 108,   1]])\n",
      "tensor([[ 85,  49, 107, 101,  89,  13, 106,  95, 108,   1]])\n",
      "tensor([[ 49, 107, 101,  89,  13, 106,  95, 108,   1,  66]])\n",
      "tensor([[ 49, 107, 101,  89,  13, 106,  95, 108,   1,  66]])\n",
      "tensor([[107, 101,  89,  13, 106,  95, 108,   1,  66, 105]])\n",
      "tensor([[107, 101,  89,  13, 106,  95, 108,   1,  66, 105]])\n",
      "tensor([[101,  89,  13, 106,  95, 108,   1,  66, 105,  28]])\n",
      "tensor([[101,  89,  13, 106,  95, 108,   1,  66, 105,  28]])\n",
      "tensor([[ 89,  13, 106,  95, 108,   1,  66, 105,  28,  11]])\n",
      "tensor([[ 89,  13, 106,  95, 108,   1,  66, 105,  28,  11]])\n",
      "tensor([[ 13, 106,  95, 108,   1,  66, 105,  28,  11,  77]])\n",
      "tensor([[ 13, 106,  95, 108,   1,  66, 105,  28,  11,  77]])\n",
      "tensor([[106,  95, 108,   1,  66, 105,  28,  11,  77,  56]])\n",
      "tensor([[106,  95, 108,   1,  66, 105,  28,  11,  77,  56]])\n",
      "tensor([[ 95, 108,   1,  66, 105,  28,  11,  77,  56,   6]])\n",
      "tensor([[ 95, 108,   1,  66, 105,  28,  11,  77,  56,   6]])\n",
      "tensor([[108,   1,  66, 105,  28,  11,  77,  56,   6,  31]])\n",
      "tensor([[108,   1,  66, 105,  28,  11,  77,  56,   6,  31]])\n",
      "tensor([[  1,  66, 105,  28,  11,  77,  56,   6,  31,  88]])\n",
      "tensor([[  1,  66, 105,  28,  11,  77,  56,   6,  31,  88]])\n",
      "tensor([[ 66, 105,  28,  11,  77,  56,   6,  31,  88,  60]])\n",
      "tensor([[ 66, 105,  28,  11,  77,  56,   6,  31,  88,  60]])\n",
      "tensor([[105,  28,  11,  77,  56,   6,  31,  88,  60,  41]])\n",
      "tensor([[105,  28,  11,  77,  56,   6,  31,  88,  60,  41]])\n",
      "tensor([[ 28,  11,  77,  56,   6,  31,  88,  60,  41, 105]])\n",
      "tensor([[ 28,  11,  77,  56,   6,  31,  88,  60,  41, 105]])\n",
      "tensor([[ 11,  77,  56,   6,  31,  88,  60,  41, 105,  21]])\n",
      "tensor([[ 11,  77,  56,   6,  31,  88,  60,  41, 105,  21]])\n",
      "tensor([[ 77,  56,   6,  31,  88,  60,  41, 105,  21,  29]])\n",
      "tensor([[ 77,  56,   6,  31,  88,  60,  41, 105,  21,  29]])\n",
      "tensor([[ 56,   6,  31,  88,  60,  41, 105,  21,  29,  61]])\n",
      "tensor([[ 56,   6,  31,  88,  60,  41, 105,  21,  29,  61]])\n",
      "tensor([[  6,  31,  88,  60,  41, 105,  21,  29,  61,  94]])\n",
      "tensor([[  6,  31,  88,  60,  41, 105,  21,  29,  61,  94]])\n",
      "tensor([[ 31,  88,  60,  41, 105,  21,  29,  61,  94,  89]])\n",
      "tensor([[ 31,  88,  60,  41, 105,  21,  29,  61,  94,  89]])\n",
      "tensor([[ 88,  60,  41, 105,  21,  29,  61,  94,  89,  13]])\n",
      "tensor([[ 88,  60,  41, 105,  21,  29,  61,  94,  89,  13]])\n",
      "tensor([[ 60,  41, 105,  21,  29,  61,  94,  89,  13,  82]])\n",
      "tensor([[ 60,  41, 105,  21,  29,  61,  94,  89,  13,  82]])\n",
      "tensor([[ 41, 105,  21,  29,  61,  94,  89,  13,  82,  78]])\n",
      "tensor([[ 41, 105,  21,  29,  61,  94,  89,  13,  82,  78]])\n",
      "tensor([[105,  21,  29,  61,  94,  89,  13,  82,  78,   2]])\n",
      "tensor([[105,  21,  29,  61,  94,  89,  13,  82,  78,   2]])\n",
      "tensor([[21, 29, 61, 94, 89, 13, 82, 78,  2,  4]])\n",
      "tensor([[21, 29, 61, 94, 89, 13, 82, 78,  2,  4]])\n",
      "tensor([[ 29,  61,  94,  89,  13,  82,  78,   2,   4, 105]])\n",
      "tensor([[ 29,  61,  94,  89,  13,  82,  78,   2,   4, 105]])\n",
      "tensor([[ 61,  94,  89,  13,  82,  78,   2,   4, 105,  46]])\n",
      "tensor([[ 61,  94,  89,  13,  82,  78,   2,   4, 105,  46]])\n",
      "tensor([[ 94,  89,  13,  82,  78,   2,   4, 105,  46,  39]])\n",
      "tensor([[ 94,  89,  13,  82,  78,   2,   4, 105,  46,  39]])\n",
      "tensor([[ 89,  13,  82,  78,   2,   4, 105,  46,  39, 103]])\n",
      "tensor([[ 89,  13,  82,  78,   2,   4, 105,  46,  39, 103]])\n",
      "tensor([[ 13,  82,  78,   2,   4, 105,  46,  39, 103,  40]])\n",
      "tensor([[ 13,  82,  78,   2,   4, 105,  46,  39, 103,  40]])\n",
      "tensor([[ 82,  78,   2,   4, 105,  46,  39, 103,  40, 105]])\n",
      "tensor([[ 82,  78,   2,   4, 105,  46,  39, 103,  40, 105]])\n",
      "tensor([[ 78,   2,   4, 105,  46,  39, 103,  40, 105,  73]])\n",
      "tensor([[ 78,   2,   4, 105,  46,  39, 103,  40, 105,  73]])\n",
      "tensor([[  2,   4, 105,  46,  39, 103,  40, 105,  73,  72]])\n",
      "tensor([[  2,   4, 105,  46,  39, 103,  40, 105,  73,  72]])\n",
      "tensor([[  4, 105,  46,  39, 103,  40, 105,  73,  72,  97]])\n",
      "tensor([[  4, 105,  46,  39, 103,  40, 105,  73,  72,  97]])\n",
      "tensor([[105,  46,  39, 103,  40, 105,  73,  72,  97,  66]])\n",
      "tensor([[105,  46,  39, 103,  40, 105,  73,  72,  97,  66]])\n",
      "tensor([[ 46,  39, 103,  40, 105,  73,  72,  97,  66,  38]])\n",
      "tensor([[ 46,  39, 103,  40, 105,  73,  72,  97,  66,  38]])\n",
      "tensor([[ 39, 103,  40, 105,  73,  72,  97,  66,  38,  36]])\n",
      "tensor([[ 39, 103,  40, 105,  73,  72,  97,  66,  38,  36]])\n",
      "tensor([[103,  40, 105,  73,  72,  97,  66,  38,  36,  23]])\n",
      "tensor([[103,  40, 105,  73,  72,  97,  66,  38,  36,  23]])\n",
      "tensor([[ 40, 105,  73,  72,  97,  66,  38,  36,  23,  89]])\n",
      "tensor([[ 40, 105,  73,  72,  97,  66,  38,  36,  23,  89]])\n",
      "tensor([[105,  73,  72,  97,  66,  38,  36,  23,  89,  13]])\n",
      "tensor([[105,  73,  72,  97,  66,  38,  36,  23,  89,  13]])\n",
      "tensor([[ 73,  72,  97,  66,  38,  36,  23,  89,  13, 100]])\n",
      "tensor([[ 73,  72,  97,  66,  38,  36,  23,  89,  13, 100]])\n",
      "tensor([[ 72,  97,  66,  38,  36,  23,  89,  13, 100,  26]])\n",
      "tensor([[ 72,  97,  66,  38,  36,  23,  89,  13, 100,  26]])\n",
      "tensor([[ 97,  66,  38,  36,  23,  89,  13, 100,  26,  42]])\n",
      "tensor([[ 97,  66,  38,  36,  23,  89,  13, 100,  26,  42]])\n",
      "tensor([[ 66,  38,  36,  23,  89,  13, 100,  26,  42, 105]])\n",
      "tensor([[ 66,  38,  36,  23,  89,  13, 100,  26,  42, 105]])\n",
      "tensor([[ 38,  36,  23,  89,  13, 100,  26,  42, 105,  50]])\n",
      "tensor([[ 38,  36,  23,  89,  13, 100,  26,  42, 105,  50]])\n",
      "tensor([[ 36,  23,  89,  13, 100,  26,  42, 105,  50,  44]])\n",
      "tensor([[ 36,  23,  89,  13, 100,  26,  42, 105,  50,  44]])\n",
      "tensor([[ 23,  89,  13, 100,  26,  42, 105,  50,  44,  67]])\n",
      "tensor([[ 23,  89,  13, 100,  26,  42, 105,  50,  44,  67]])\n",
      "tensor([[ 89,  13, 100,  26,  42, 105,  50,  44,  67,  84]])\n",
      "tensor([[ 89,  13, 100,  26,  42, 105,  50,  44,  67,  84]])\n",
      "tensor([[ 13, 100,  26,  42, 105,  50,  44,  67,  84,   5]])\n",
      "tensor([[ 13, 100,  26,  42, 105,  50,  44,  67,  84,   5]])\n",
      "tensor([[100,  26,  42, 105,  50,  44,  67,  84,   5, 105]])\n",
      "tensor([[100,  26,  42, 105,  50,  44,  67,  84,   5, 105]])\n",
      "tensor([[ 26,  42, 105,  50,  44,  67,  84,   5, 105,  57]])\n",
      "tensor([[ 26,  42, 105,  50,  44,  67,  84,   5, 105,  57]])\n",
      "tensor([[ 42, 105,  50,  44,  67,  84,   5, 105,  57,  98]])\n",
      "tensor([[ 42, 105,  50,  44,  67,  84,   5, 105,  57,  98]])\n",
      "tensor([[105,  50,  44,  67,  84,   5, 105,  57,  98,  54]])\n",
      "tensor([[105,  50,  44,  67,  84,   5, 105,  57,  98,  54]])\n",
      "tensor([[ 50,  44,  67,  84,   5, 105,  57,  98,  54,  12]])\n",
      "tensor([[ 50,  44,  67,  84,   5, 105,  57,  98,  54,  12]])\n",
      "tensor([[ 44,  67,  84,   5, 105,  57,  98,  54,  12,  18]])\n",
      "tensor([[ 44,  67,  84,   5, 105,  57,  98,  54,  12,  18]])\n",
      "tensor([[ 67,  84,   5, 105,  57,  98,  54,  12,  18,  13]])\n"
     ]
    }
   ],
   "source": [
    "for input_seq, target_seq in dataloader:\n",
    "    print(input_seq)\n",
    "    print(target_seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d9e9fd013d3d01ba",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T04:52:40.796894Z",
     "start_time": "2025-06-30T04:52:40.791431Z"
    }
   },
   "outputs": [],
   "source": [
    "# 大都督周瑜（我的微信: dadudu6789）\n",
    "class ZhouyuRNN(nn.Module):\n",
    "    def __init__(self, vocab_size, hidden_size):\n",
    "        super().__init__()\n",
    "        self.hidden_size = hidden_size\n",
    "\n",
    "        # 嵌入层，输入词索引，输出词向量，词向量的大小就是hidden_size\n",
    "        self.embedding = nn.Embedding(vocab_size, hidden_size)\n",
    "\n",
    "        # RNN参数\n",
    "        # input_size表示输入的x的大小，一个词就是词向量，而这里词向量的大小就是hidden_size\n",
    "        self.rnn = nn.RNN(input_size=hidden_size, hidden_size=hidden_size, num_layers=2, batch_first=True, nonlinearity='tanh')\n",
    "\n",
    "        # 输出层\n",
    "        self.out_linear = nn.Linear(hidden_size, vocab_size)\n",
    "\n",
    "    def forward(self, x, hidden=None):\n",
    "\n",
    "        embedded = self.embedding(x) # x是一个seq，每个词的索引\n",
    "\n",
    "        batch_size, seq_len, hidden_size = embedded.shape\n",
    "\n",
    "        # 初始化隐藏状态，每个seq都创建一个初始隐藏状态\n",
    "        # 2表示num_layers\n",
    "        if hidden is None:\n",
    "            hidden = torch.zeros(2, batch_size, hidden_size)\n",
    "\n",
    "        outputs, hidden = self.rnn(embedded, hidden)\n",
    "\n",
    "        out = self.out_linear(outputs)\n",
    "        return out, hidden\n",
    "\n",
    "\n",
    "# 初始化模型\n",
    "model = ZhouyuRNN(vocab_size, HIDDEN_SIZE)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.005)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2348428ce74982e4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T04:52:57.108851Z",
     "start_time": "2025-06-30T04:52:42.563158Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/100], Step [20/138], Loss: 4.5686\n",
      "Epoch [1/100], Step [40/138], Loss: 4.6219\n",
      "Epoch [1/100], Step [60/138], Loss: 4.5796\n",
      "Epoch [1/100], Step [80/138], Loss: 4.5511\n",
      "Epoch [1/100], Step [100/138], Loss: 4.6859\n",
      "Epoch [1/100], Step [120/138], Loss: 4.7132\n",
      "Epoch [2/100], Step [20/138], Loss: 4.3381\n",
      "Epoch [2/100], Step [40/138], Loss: 4.3978\n",
      "Epoch [2/100], Step [60/138], Loss: 4.3421\n",
      "Epoch [2/100], Step [80/138], Loss: 4.3422\n",
      "Epoch [2/100], Step [100/138], Loss: 4.4919\n",
      "Epoch [2/100], Step [120/138], Loss: 4.5095\n",
      "Epoch [3/100], Step [20/138], Loss: 4.0978\n",
      "Epoch [3/100], Step [40/138], Loss: 4.1582\n",
      "Epoch [3/100], Step [60/138], Loss: 4.0836\n",
      "Epoch [3/100], Step [80/138], Loss: 4.1267\n",
      "Epoch [3/100], Step [100/138], Loss: 4.2846\n",
      "Epoch [3/100], Step [120/138], Loss: 4.2877\n",
      "Epoch [4/100], Step [20/138], Loss: 3.8421\n",
      "Epoch [4/100], Step [40/138], Loss: 3.8859\n",
      "Epoch [4/100], Step [60/138], Loss: 3.7902\n",
      "Epoch [4/100], Step [80/138], Loss: 3.8992\n",
      "Epoch [4/100], Step [100/138], Loss: 4.0549\n",
      "Epoch [4/100], Step [120/138], Loss: 4.0413\n",
      "Epoch [5/100], Step [20/138], Loss: 3.5861\n",
      "Epoch [5/100], Step [40/138], Loss: 3.5817\n",
      "Epoch [5/100], Step [60/138], Loss: 3.4807\n",
      "Epoch [5/100], Step [80/138], Loss: 3.6603\n",
      "Epoch [5/100], Step [100/138], Loss: 3.8102\n",
      "Epoch [5/100], Step [120/138], Loss: 3.7902\n",
      "Epoch [6/100], Step [20/138], Loss: 3.3641\n",
      "Epoch [6/100], Step [40/138], Loss: 3.2894\n",
      "Epoch [6/100], Step [60/138], Loss: 3.2130\n",
      "Epoch [6/100], Step [80/138], Loss: 3.4119\n",
      "Epoch [6/100], Step [100/138], Loss: 3.5767\n",
      "Epoch [6/100], Step [120/138], Loss: 3.5645\n",
      "Epoch [7/100], Step [20/138], Loss: 3.1637\n",
      "Epoch [7/100], Step [40/138], Loss: 3.0393\n",
      "Epoch [7/100], Step [60/138], Loss: 2.9995\n",
      "Epoch [7/100], Step [80/138], Loss: 3.1556\n",
      "Epoch [7/100], Step [100/138], Loss: 3.3598\n",
      "Epoch [7/100], Step [120/138], Loss: 3.3570\n",
      "Epoch [8/100], Step [20/138], Loss: 2.9662\n",
      "Epoch [8/100], Step [40/138], Loss: 2.8204\n",
      "Epoch [8/100], Step [60/138], Loss: 2.8150\n",
      "Epoch [8/100], Step [80/138], Loss: 2.8994\n",
      "Epoch [8/100], Step [100/138], Loss: 3.1533\n",
      "Epoch [8/100], Step [120/138], Loss: 3.1551\n",
      "Epoch [9/100], Step [20/138], Loss: 2.7713\n",
      "Epoch [9/100], Step [40/138], Loss: 2.6195\n",
      "Epoch [9/100], Step [60/138], Loss: 2.6443\n",
      "Epoch [9/100], Step [80/138], Loss: 2.6535\n",
      "Epoch [9/100], Step [100/138], Loss: 2.9532\n",
      "Epoch [9/100], Step [120/138], Loss: 2.9537\n",
      "Epoch [10/100], Step [20/138], Loss: 2.5787\n",
      "Epoch [10/100], Step [40/138], Loss: 2.4299\n",
      "Epoch [10/100], Step [60/138], Loss: 2.4812\n",
      "Epoch [10/100], Step [80/138], Loss: 2.4259\n",
      "Epoch [10/100], Step [100/138], Loss: 2.7567\n",
      "Epoch [10/100], Step [120/138], Loss: 2.7510\n",
      "Epoch [11/100], Step [20/138], Loss: 2.3884\n",
      "Epoch [11/100], Step [40/138], Loss: 2.2484\n",
      "Epoch [11/100], Step [60/138], Loss: 2.3236\n",
      "Epoch [11/100], Step [80/138], Loss: 2.2178\n",
      "Epoch [11/100], Step [100/138], Loss: 2.5629\n",
      "Epoch [11/100], Step [120/138], Loss: 2.5470\n",
      "Epoch [12/100], Step [20/138], Loss: 2.2014\n",
      "Epoch [12/100], Step [40/138], Loss: 2.0739\n",
      "Epoch [12/100], Step [60/138], Loss: 2.1704\n",
      "Epoch [12/100], Step [80/138], Loss: 2.0253\n",
      "Epoch [12/100], Step [100/138], Loss: 2.3719\n",
      "Epoch [12/100], Step [120/138], Loss: 2.3439\n",
      "Epoch [13/100], Step [20/138], Loss: 2.0201\n",
      "Epoch [13/100], Step [40/138], Loss: 1.9065\n",
      "Epoch [13/100], Step [60/138], Loss: 2.0216\n",
      "Epoch [13/100], Step [80/138], Loss: 1.8448\n",
      "Epoch [13/100], Step [100/138], Loss: 2.1851\n",
      "Epoch [13/100], Step [120/138], Loss: 2.1445\n",
      "Epoch [14/100], Step [20/138], Loss: 1.8472\n",
      "Epoch [14/100], Step [40/138], Loss: 1.7464\n",
      "Epoch [14/100], Step [60/138], Loss: 1.8771\n",
      "Epoch [14/100], Step [80/138], Loss: 1.6748\n",
      "Epoch [14/100], Step [100/138], Loss: 2.0039\n",
      "Epoch [14/100], Step [120/138], Loss: 1.9522\n",
      "Epoch [15/100], Step [20/138], Loss: 1.6854\n",
      "Epoch [15/100], Step [40/138], Loss: 1.5944\n",
      "Epoch [15/100], Step [60/138], Loss: 1.7374\n",
      "Epoch [15/100], Step [80/138], Loss: 1.5150\n",
      "Epoch [15/100], Step [100/138], Loss: 1.8300\n",
      "Epoch [15/100], Step [120/138], Loss: 1.7701\n",
      "Epoch [16/100], Step [20/138], Loss: 1.5367\n",
      "Epoch [16/100], Step [40/138], Loss: 1.4513\n",
      "Epoch [16/100], Step [60/138], Loss: 1.6032\n",
      "Epoch [16/100], Step [80/138], Loss: 1.3660\n",
      "Epoch [16/100], Step [100/138], Loss: 1.6649\n",
      "Epoch [16/100], Step [120/138], Loss: 1.6003\n",
      "Epoch [17/100], Step [20/138], Loss: 1.4019\n",
      "Epoch [17/100], Step [40/138], Loss: 1.3179\n",
      "Epoch [17/100], Step [60/138], Loss: 1.4753\n",
      "Epoch [17/100], Step [80/138], Loss: 1.2286\n",
      "Epoch [17/100], Step [100/138], Loss: 1.5102\n",
      "Epoch [17/100], Step [120/138], Loss: 1.4439\n",
      "Epoch [18/100], Step [20/138], Loss: 1.2810\n",
      "Epoch [18/100], Step [40/138], Loss: 1.1946\n",
      "Epoch [18/100], Step [60/138], Loss: 1.3544\n",
      "Epoch [18/100], Step [80/138], Loss: 1.1034\n",
      "Epoch [18/100], Step [100/138], Loss: 1.3666\n",
      "Epoch [18/100], Step [120/138], Loss: 1.3015\n",
      "Epoch [19/100], Step [20/138], Loss: 1.1734\n",
      "Epoch [19/100], Step [40/138], Loss: 1.0818\n",
      "Epoch [19/100], Step [60/138], Loss: 1.2412\n",
      "Epoch [19/100], Step [80/138], Loss: 0.9907\n",
      "Epoch [19/100], Step [100/138], Loss: 1.2349\n",
      "Epoch [19/100], Step [120/138], Loss: 1.1730\n",
      "Epoch [20/100], Step [20/138], Loss: 1.0780\n",
      "Epoch [20/100], Step [40/138], Loss: 0.9793\n",
      "Epoch [20/100], Step [60/138], Loss: 1.1363\n",
      "Epoch [20/100], Step [80/138], Loss: 0.8903\n",
      "Epoch [20/100], Step [100/138], Loss: 1.1154\n",
      "Epoch [20/100], Step [120/138], Loss: 1.0579\n",
      "Epoch [21/100], Step [20/138], Loss: 0.9936\n",
      "Epoch [21/100], Step [40/138], Loss: 0.8870\n",
      "Epoch [21/100], Step [60/138], Loss: 1.0399\n",
      "Epoch [21/100], Step [80/138], Loss: 0.8017\n",
      "Epoch [21/100], Step [100/138], Loss: 1.0079\n",
      "Epoch [21/100], Step [120/138], Loss: 0.9555\n",
      "Epoch [22/100], Step [20/138], Loss: 0.9189\n",
      "Epoch [22/100], Step [40/138], Loss: 0.8042\n",
      "Epoch [22/100], Step [60/138], Loss: 0.9520\n",
      "Epoch [22/100], Step [80/138], Loss: 0.7238\n",
      "Epoch [22/100], Step [100/138], Loss: 0.9120\n",
      "Epoch [22/100], Step [120/138], Loss: 0.8649\n",
      "Epoch [23/100], Step [20/138], Loss: 0.8527\n",
      "Epoch [23/100], Step [40/138], Loss: 0.7303\n",
      "Epoch [23/100], Step [60/138], Loss: 0.8723\n",
      "Epoch [23/100], Step [80/138], Loss: 0.6555\n",
      "Epoch [23/100], Step [100/138], Loss: 0.8269\n",
      "Epoch [23/100], Step [120/138], Loss: 0.7851\n",
      "Epoch [24/100], Step [20/138], Loss: 0.7940\n",
      "Epoch [24/100], Step [40/138], Loss: 0.6646\n",
      "Epoch [24/100], Step [60/138], Loss: 0.8005\n",
      "Epoch [24/100], Step [80/138], Loss: 0.5959\n",
      "Epoch [24/100], Step [100/138], Loss: 0.7517\n",
      "Epoch [24/100], Step [120/138], Loss: 0.7151\n",
      "Epoch [25/100], Step [20/138], Loss: 0.7417\n",
      "Epoch [25/100], Step [40/138], Loss: 0.6063\n",
      "Epoch [25/100], Step [60/138], Loss: 0.7360\n",
      "Epoch [25/100], Step [80/138], Loss: 0.5437\n",
      "Epoch [25/100], Step [100/138], Loss: 0.6855\n",
      "Epoch [25/100], Step [120/138], Loss: 0.6536\n",
      "Epoch [26/100], Step [20/138], Loss: 0.6950\n",
      "Epoch [26/100], Step [40/138], Loss: 0.5546\n",
      "Epoch [26/100], Step [60/138], Loss: 0.6780\n",
      "Epoch [26/100], Step [80/138], Loss: 0.4979\n",
      "Epoch [26/100], Step [100/138], Loss: 0.6271\n",
      "Epoch [26/100], Step [120/138], Loss: 0.5997\n",
      "Epoch [27/100], Step [20/138], Loss: 0.6532\n",
      "Epoch [27/100], Step [40/138], Loss: 0.5088\n",
      "Epoch [27/100], Step [60/138], Loss: 0.6261\n",
      "Epoch [27/100], Step [80/138], Loss: 0.4577\n",
      "Epoch [27/100], Step [100/138], Loss: 0.5757\n",
      "Epoch [27/100], Step [120/138], Loss: 0.5524\n",
      "Epoch [28/100], Step [20/138], Loss: 0.6156\n",
      "Epoch [28/100], Step [40/138], Loss: 0.4682\n",
      "Epoch [28/100], Step [60/138], Loss: 0.5795\n",
      "Epoch [28/100], Step [80/138], Loss: 0.4222\n",
      "Epoch [28/100], Step [100/138], Loss: 0.5304\n",
      "Epoch [28/100], Step [120/138], Loss: 0.5107\n",
      "Epoch [29/100], Step [20/138], Loss: 0.5818\n",
      "Epoch [29/100], Step [40/138], Loss: 0.4321\n",
      "Epoch [29/100], Step [60/138], Loss: 0.5378\n",
      "Epoch [29/100], Step [80/138], Loss: 0.3907\n",
      "Epoch [29/100], Step [100/138], Loss: 0.4902\n",
      "Epoch [29/100], Step [120/138], Loss: 0.4739\n",
      "Epoch [30/100], Step [20/138], Loss: 0.5513\n",
      "Epoch [30/100], Step [40/138], Loss: 0.4000\n",
      "Epoch [30/100], Step [60/138], Loss: 0.5002\n",
      "Epoch [30/100], Step [80/138], Loss: 0.3627\n",
      "Epoch [30/100], Step [100/138], Loss: 0.4547\n",
      "Epoch [30/100], Step [120/138], Loss: 0.4412\n",
      "Epoch [31/100], Step [20/138], Loss: 0.5236\n",
      "Epoch [31/100], Step [40/138], Loss: 0.3713\n",
      "Epoch [31/100], Step [60/138], Loss: 0.4665\n",
      "Epoch [31/100], Step [80/138], Loss: 0.3378\n",
      "Epoch [31/100], Step [100/138], Loss: 0.4231\n",
      "Epoch [31/100], Step [120/138], Loss: 0.4122\n",
      "Epoch [32/100], Step [20/138], Loss: 0.4985\n",
      "Epoch [32/100], Step [40/138], Loss: 0.3457\n",
      "Epoch [32/100], Step [60/138], Loss: 0.4361\n",
      "Epoch [32/100], Step [80/138], Loss: 0.3155\n",
      "Epoch [32/100], Step [100/138], Loss: 0.3949\n",
      "Epoch [32/100], Step [120/138], Loss: 0.3863\n",
      "Epoch [33/100], Step [20/138], Loss: 0.4756\n",
      "Epoch [33/100], Step [40/138], Loss: 0.3227\n",
      "Epoch [33/100], Step [60/138], Loss: 0.4087\n",
      "Epoch [33/100], Step [80/138], Loss: 0.2954\n",
      "Epoch [33/100], Step [100/138], Loss: 0.3696\n",
      "Epoch [33/100], Step [120/138], Loss: 0.3631\n",
      "Epoch [34/100], Step [20/138], Loss: 0.4547\n",
      "Epoch [34/100], Step [40/138], Loss: 0.3020\n",
      "Epoch [34/100], Step [60/138], Loss: 0.3839\n",
      "Epoch [34/100], Step [80/138], Loss: 0.2773\n",
      "Epoch [34/100], Step [100/138], Loss: 0.3469\n",
      "Epoch [34/100], Step [120/138], Loss: 0.3422\n",
      "Epoch [35/100], Step [20/138], Loss: 0.4357\n",
      "Epoch [35/100], Step [40/138], Loss: 0.2834\n",
      "Epoch [35/100], Step [60/138], Loss: 0.3614\n",
      "Epoch [35/100], Step [80/138], Loss: 0.2609\n",
      "Epoch [35/100], Step [100/138], Loss: 0.3265\n",
      "Epoch [35/100], Step [120/138], Loss: 0.3233\n",
      "Epoch [36/100], Step [20/138], Loss: 0.4182\n",
      "Epoch [36/100], Step [40/138], Loss: 0.2665\n",
      "Epoch [36/100], Step [60/138], Loss: 0.3410\n",
      "Epoch [36/100], Step [80/138], Loss: 0.2461\n",
      "Epoch [36/100], Step [100/138], Loss: 0.3081\n",
      "Epoch [36/100], Step [120/138], Loss: 0.3061\n",
      "Epoch [37/100], Step [20/138], Loss: 0.4021\n",
      "Epoch [37/100], Step [40/138], Loss: 0.2512\n",
      "Epoch [37/100], Step [60/138], Loss: 0.3224\n",
      "Epoch [37/100], Step [80/138], Loss: 0.2325\n",
      "Epoch [37/100], Step [100/138], Loss: 0.2913\n",
      "Epoch [37/100], Step [120/138], Loss: 0.2905\n",
      "Epoch [38/100], Step [20/138], Loss: 0.3873\n",
      "Epoch [38/100], Step [40/138], Loss: 0.2372\n",
      "Epoch [38/100], Step [60/138], Loss: 0.3054\n",
      "Epoch [38/100], Step [80/138], Loss: 0.2202\n",
      "Epoch [38/100], Step [100/138], Loss: 0.2761\n",
      "Epoch [38/100], Step [120/138], Loss: 0.2763\n",
      "Epoch [39/100], Step [20/138], Loss: 0.3736\n",
      "Epoch [39/100], Step [40/138], Loss: 0.2245\n",
      "Epoch [39/100], Step [60/138], Loss: 0.2898\n",
      "Epoch [39/100], Step [80/138], Loss: 0.2089\n",
      "Epoch [39/100], Step [100/138], Loss: 0.2622\n",
      "Epoch [39/100], Step [120/138], Loss: 0.2632\n",
      "Epoch [40/100], Step [20/138], Loss: 0.3610\n",
      "Epoch [40/100], Step [40/138], Loss: 0.2128\n",
      "Epoch [40/100], Step [60/138], Loss: 0.2756\n",
      "Epoch [40/100], Step [80/138], Loss: 0.1985\n",
      "Epoch [40/100], Step [100/138], Loss: 0.2495\n",
      "Epoch [40/100], Step [120/138], Loss: 0.2512\n",
      "Epoch [41/100], Step [20/138], Loss: 0.3492\n",
      "Epoch [41/100], Step [40/138], Loss: 0.2022\n",
      "Epoch [41/100], Step [60/138], Loss: 0.2625\n",
      "Epoch [41/100], Step [80/138], Loss: 0.1890\n",
      "Epoch [41/100], Step [100/138], Loss: 0.2378\n",
      "Epoch [41/100], Step [120/138], Loss: 0.2402\n",
      "Epoch [42/100], Step [20/138], Loss: 0.3383\n",
      "Epoch [42/100], Step [40/138], Loss: 0.1923\n",
      "Epoch [42/100], Step [60/138], Loss: 0.2504\n",
      "Epoch [42/100], Step [80/138], Loss: 0.1802\n",
      "Epoch [42/100], Step [100/138], Loss: 0.2271\n",
      "Epoch [42/100], Step [120/138], Loss: 0.2299\n",
      "Epoch [43/100], Step [20/138], Loss: 0.3282\n",
      "Epoch [43/100], Step [40/138], Loss: 0.1833\n",
      "Epoch [43/100], Step [60/138], Loss: 0.2393\n",
      "Epoch [43/100], Step [80/138], Loss: 0.1721\n",
      "Epoch [43/100], Step [100/138], Loss: 0.2171\n",
      "Epoch [43/100], Step [120/138], Loss: 0.2205\n",
      "Epoch [44/100], Step [20/138], Loss: 0.3186\n",
      "Epoch [44/100], Step [40/138], Loss: 0.1749\n",
      "Epoch [44/100], Step [60/138], Loss: 0.2290\n",
      "Epoch [44/100], Step [80/138], Loss: 0.1646\n",
      "Epoch [44/100], Step [100/138], Loss: 0.2080\n",
      "Epoch [44/100], Step [120/138], Loss: 0.2117\n",
      "Epoch [45/100], Step [20/138], Loss: 0.3098\n",
      "Epoch [45/100], Step [40/138], Loss: 0.1672\n",
      "Epoch [45/100], Step [60/138], Loss: 0.2194\n",
      "Epoch [45/100], Step [80/138], Loss: 0.1576\n",
      "Epoch [45/100], Step [100/138], Loss: 0.1995\n",
      "Epoch [45/100], Step [120/138], Loss: 0.2035\n",
      "Epoch [46/100], Step [20/138], Loss: 0.3014\n",
      "Epoch [46/100], Step [40/138], Loss: 0.1600\n",
      "Epoch [46/100], Step [60/138], Loss: 0.2105\n",
      "Epoch [46/100], Step [80/138], Loss: 0.1511\n",
      "Epoch [46/100], Step [100/138], Loss: 0.1916\n",
      "Epoch [46/100], Step [120/138], Loss: 0.1958\n",
      "Epoch [47/100], Step [20/138], Loss: 0.2936\n",
      "Epoch [47/100], Step [40/138], Loss: 0.1533\n",
      "Epoch [47/100], Step [60/138], Loss: 0.2022\n",
      "Epoch [47/100], Step [80/138], Loss: 0.1451\n",
      "Epoch [47/100], Step [100/138], Loss: 0.1842\n",
      "Epoch [47/100], Step [120/138], Loss: 0.1887\n",
      "Epoch [48/100], Step [20/138], Loss: 0.2862\n",
      "Epoch [48/100], Step [40/138], Loss: 0.1471\n",
      "Epoch [48/100], Step [60/138], Loss: 0.1945\n",
      "Epoch [48/100], Step [80/138], Loss: 0.1394\n",
      "Epoch [48/100], Step [100/138], Loss: 0.1773\n",
      "Epoch [48/100], Step [120/138], Loss: 0.1820\n",
      "Epoch [49/100], Step [20/138], Loss: 0.2792\n",
      "Epoch [49/100], Step [40/138], Loss: 0.1413\n",
      "Epoch [49/100], Step [60/138], Loss: 0.1873\n",
      "Epoch [49/100], Step [80/138], Loss: 0.1341\n",
      "Epoch [49/100], Step [100/138], Loss: 0.1709\n",
      "Epoch [49/100], Step [120/138], Loss: 0.1757\n",
      "Epoch [50/100], Step [20/138], Loss: 0.2726\n",
      "Epoch [50/100], Step [40/138], Loss: 0.1359\n",
      "Epoch [50/100], Step [60/138], Loss: 0.1805\n",
      "Epoch [50/100], Step [80/138], Loss: 0.1292\n",
      "Epoch [50/100], Step [100/138], Loss: 0.1649\n",
      "Epoch [50/100], Step [120/138], Loss: 0.1697\n",
      "Epoch [51/100], Step [20/138], Loss: 0.2664\n",
      "Epoch [51/100], Step [40/138], Loss: 0.1308\n",
      "Epoch [51/100], Step [60/138], Loss: 0.1742\n",
      "Epoch [51/100], Step [80/138], Loss: 0.1246\n",
      "Epoch [51/100], Step [100/138], Loss: 0.1593\n",
      "Epoch [51/100], Step [120/138], Loss: 0.1642\n",
      "Epoch [52/100], Step [20/138], Loss: 0.2605\n",
      "Epoch [52/100], Step [40/138], Loss: 0.1261\n",
      "Epoch [52/100], Step [60/138], Loss: 0.1682\n",
      "Epoch [52/100], Step [80/138], Loss: 0.1202\n",
      "Epoch [52/100], Step [100/138], Loss: 0.1539\n",
      "Epoch [52/100], Step [120/138], Loss: 0.1589\n",
      "Epoch [53/100], Step [20/138], Loss: 0.2549\n",
      "Epoch [53/100], Step [40/138], Loss: 0.1216\n",
      "Epoch [53/100], Step [60/138], Loss: 0.1626\n",
      "Epoch [53/100], Step [80/138], Loss: 0.1161\n",
      "Epoch [53/100], Step [100/138], Loss: 0.1489\n",
      "Epoch [53/100], Step [120/138], Loss: 0.1540\n",
      "Epoch [54/100], Step [20/138], Loss: 0.2496\n",
      "Epoch [54/100], Step [40/138], Loss: 0.1174\n",
      "Epoch [54/100], Step [60/138], Loss: 0.1573\n",
      "Epoch [54/100], Step [80/138], Loss: 0.1123\n",
      "Epoch [54/100], Step [100/138], Loss: 0.1442\n",
      "Epoch [54/100], Step [120/138], Loss: 0.1493\n",
      "Epoch [55/100], Step [20/138], Loss: 0.2445\n",
      "Epoch [55/100], Step [40/138], Loss: 0.1134\n",
      "Epoch [55/100], Step [60/138], Loss: 0.1523\n",
      "Epoch [55/100], Step [80/138], Loss: 0.1086\n",
      "Epoch [55/100], Step [100/138], Loss: 0.1398\n",
      "Epoch [55/100], Step [120/138], Loss: 0.1448\n",
      "Epoch [56/100], Step [20/138], Loss: 0.2397\n",
      "Epoch [56/100], Step [40/138], Loss: 0.1097\n",
      "Epoch [56/100], Step [60/138], Loss: 0.1476\n",
      "Epoch [56/100], Step [80/138], Loss: 0.1052\n",
      "Epoch [56/100], Step [100/138], Loss: 0.1356\n",
      "Epoch [56/100], Step [120/138], Loss: 0.1406\n",
      "Epoch [57/100], Step [20/138], Loss: 0.2351\n",
      "Epoch [57/100], Step [40/138], Loss: 0.1062\n",
      "Epoch [57/100], Step [60/138], Loss: 0.1432\n",
      "Epoch [57/100], Step [80/138], Loss: 0.1020\n",
      "Epoch [57/100], Step [100/138], Loss: 0.1316\n",
      "Epoch [57/100], Step [120/138], Loss: 0.1366\n",
      "Epoch [58/100], Step [20/138], Loss: 0.2307\n",
      "Epoch [58/100], Step [40/138], Loss: 0.1028\n",
      "Epoch [58/100], Step [60/138], Loss: 0.1389\n",
      "Epoch [58/100], Step [80/138], Loss: 0.0989\n",
      "Epoch [58/100], Step [100/138], Loss: 0.1278\n",
      "Epoch [58/100], Step [120/138], Loss: 0.1328\n",
      "Epoch [59/100], Step [20/138], Loss: 0.2265\n",
      "Epoch [59/100], Step [40/138], Loss: 0.0997\n",
      "Epoch [59/100], Step [60/138], Loss: 0.1349\n",
      "Epoch [59/100], Step [80/138], Loss: 0.0960\n",
      "Epoch [59/100], Step [100/138], Loss: 0.1242\n",
      "Epoch [59/100], Step [120/138], Loss: 0.1291\n",
      "Epoch [60/100], Step [20/138], Loss: 0.2225\n",
      "Epoch [60/100], Step [40/138], Loss: 0.0967\n",
      "Epoch [60/100], Step [60/138], Loss: 0.1311\n",
      "Epoch [60/100], Step [80/138], Loss: 0.0932\n",
      "Epoch [60/100], Step [100/138], Loss: 0.1208\n",
      "Epoch [60/100], Step [120/138], Loss: 0.1257\n",
      "Epoch [61/100], Step [20/138], Loss: 0.2187\n",
      "Epoch [61/100], Step [40/138], Loss: 0.0938\n",
      "Epoch [61/100], Step [60/138], Loss: 0.1275\n",
      "Epoch [61/100], Step [80/138], Loss: 0.0906\n",
      "Epoch [61/100], Step [100/138], Loss: 0.1176\n",
      "Epoch [61/100], Step [120/138], Loss: 0.1224\n",
      "Epoch [62/100], Step [20/138], Loss: 0.2150\n",
      "Epoch [62/100], Step [40/138], Loss: 0.0911\n",
      "Epoch [62/100], Step [60/138], Loss: 0.1240\n",
      "Epoch [62/100], Step [80/138], Loss: 0.0881\n",
      "Epoch [62/100], Step [100/138], Loss: 0.1145\n",
      "Epoch [62/100], Step [120/138], Loss: 0.1192\n",
      "Epoch [63/100], Step [20/138], Loss: 0.2115\n",
      "Epoch [63/100], Step [40/138], Loss: 0.0886\n",
      "Epoch [63/100], Step [60/138], Loss: 0.1207\n",
      "Epoch [63/100], Step [80/138], Loss: 0.0857\n",
      "Epoch [63/100], Step [100/138], Loss: 0.1116\n",
      "Epoch [63/100], Step [120/138], Loss: 0.1162\n",
      "Epoch [64/100], Step [20/138], Loss: 0.2081\n",
      "Epoch [64/100], Step [40/138], Loss: 0.0861\n",
      "Epoch [64/100], Step [60/138], Loss: 0.1176\n",
      "Epoch [64/100], Step [80/138], Loss: 0.0835\n",
      "Epoch [64/100], Step [100/138], Loss: 0.1088\n",
      "Epoch [64/100], Step [120/138], Loss: 0.1133\n",
      "Epoch [65/100], Step [20/138], Loss: 0.2049\n",
      "Epoch [65/100], Step [40/138], Loss: 0.0838\n",
      "Epoch [65/100], Step [60/138], Loss: 0.1146\n",
      "Epoch [65/100], Step [80/138], Loss: 0.0813\n",
      "Epoch [65/100], Step [100/138], Loss: 0.1061\n",
      "Epoch [65/100], Step [120/138], Loss: 0.1106\n",
      "Epoch [66/100], Step [20/138], Loss: 0.2017\n",
      "Epoch [66/100], Step [40/138], Loss: 0.0816\n",
      "Epoch [66/100], Step [60/138], Loss: 0.1117\n",
      "Epoch [66/100], Step [80/138], Loss: 0.0792\n",
      "Epoch [66/100], Step [100/138], Loss: 0.1035\n",
      "Epoch [66/100], Step [120/138], Loss: 0.1079\n",
      "Epoch [67/100], Step [20/138], Loss: 0.1987\n",
      "Epoch [67/100], Step [40/138], Loss: 0.0795\n",
      "Epoch [67/100], Step [60/138], Loss: 0.1090\n",
      "Epoch [67/100], Step [80/138], Loss: 0.0773\n",
      "Epoch [67/100], Step [100/138], Loss: 0.1011\n",
      "Epoch [67/100], Step [120/138], Loss: 0.1054\n",
      "Epoch [68/100], Step [20/138], Loss: 0.1959\n",
      "Epoch [68/100], Step [40/138], Loss: 0.0775\n",
      "Epoch [68/100], Step [60/138], Loss: 0.1064\n",
      "Epoch [68/100], Step [80/138], Loss: 0.0754\n",
      "Epoch [68/100], Step [100/138], Loss: 0.0987\n",
      "Epoch [68/100], Step [120/138], Loss: 0.1030\n",
      "Epoch [69/100], Step [20/138], Loss: 0.1931\n",
      "Epoch [69/100], Step [40/138], Loss: 0.0755\n",
      "Epoch [69/100], Step [60/138], Loss: 0.1038\n",
      "Epoch [69/100], Step [80/138], Loss: 0.0736\n",
      "Epoch [69/100], Step [100/138], Loss: 0.0965\n",
      "Epoch [69/100], Step [120/138], Loss: 0.1006\n",
      "Epoch [70/100], Step [20/138], Loss: 0.1904\n",
      "Epoch [70/100], Step [40/138], Loss: 0.0737\n",
      "Epoch [70/100], Step [60/138], Loss: 0.1014\n",
      "Epoch [70/100], Step [80/138], Loss: 0.0719\n",
      "Epoch [70/100], Step [100/138], Loss: 0.0943\n",
      "Epoch [70/100], Step [120/138], Loss: 0.0984\n",
      "Epoch [71/100], Step [20/138], Loss: 0.1878\n",
      "Epoch [71/100], Step [40/138], Loss: 0.0719\n",
      "Epoch [71/100], Step [60/138], Loss: 0.0991\n",
      "Epoch [71/100], Step [80/138], Loss: 0.0702\n",
      "Epoch [71/100], Step [100/138], Loss: 0.0922\n",
      "Epoch [71/100], Step [120/138], Loss: 0.0962\n",
      "Epoch [72/100], Step [20/138], Loss: 0.1854\n",
      "Epoch [72/100], Step [40/138], Loss: 0.0702\n",
      "Epoch [72/100], Step [60/138], Loss: 0.0969\n",
      "Epoch [72/100], Step [80/138], Loss: 0.0686\n",
      "Epoch [72/100], Step [100/138], Loss: 0.0902\n",
      "Epoch [72/100], Step [120/138], Loss: 0.0942\n",
      "Epoch [73/100], Step [20/138], Loss: 0.1830\n",
      "Epoch [73/100], Step [40/138], Loss: 0.0686\n",
      "Epoch [73/100], Step [60/138], Loss: 0.0948\n",
      "Epoch [73/100], Step [80/138], Loss: 0.0671\n",
      "Epoch [73/100], Step [100/138], Loss: 0.0883\n",
      "Epoch [73/100], Step [120/138], Loss: 0.0922\n",
      "Epoch [74/100], Step [20/138], Loss: 0.1807\n",
      "Epoch [74/100], Step [40/138], Loss: 0.0671\n",
      "Epoch [74/100], Step [60/138], Loss: 0.0927\n",
      "Epoch [74/100], Step [80/138], Loss: 0.0656\n",
      "Epoch [74/100], Step [100/138], Loss: 0.0865\n",
      "Epoch [74/100], Step [120/138], Loss: 0.0903\n",
      "Epoch [75/100], Step [20/138], Loss: 0.1784\n",
      "Epoch [75/100], Step [40/138], Loss: 0.0656\n",
      "Epoch [75/100], Step [60/138], Loss: 0.0907\n",
      "Epoch [75/100], Step [80/138], Loss: 0.0642\n",
      "Epoch [75/100], Step [100/138], Loss: 0.0847\n",
      "Epoch [75/100], Step [120/138], Loss: 0.0884\n",
      "Epoch [76/100], Step [20/138], Loss: 0.1763\n",
      "Epoch [76/100], Step [40/138], Loss: 0.0641\n",
      "Epoch [76/100], Step [60/138], Loss: 0.0888\n",
      "Epoch [76/100], Step [80/138], Loss: 0.0629\n",
      "Epoch [76/100], Step [100/138], Loss: 0.0830\n",
      "Epoch [76/100], Step [120/138], Loss: 0.0866\n",
      "Epoch [77/100], Step [20/138], Loss: 0.1742\n",
      "Epoch [77/100], Step [40/138], Loss: 0.0628\n",
      "Epoch [77/100], Step [60/138], Loss: 0.0870\n",
      "Epoch [77/100], Step [80/138], Loss: 0.0616\n",
      "Epoch [77/100], Step [100/138], Loss: 0.0814\n",
      "Epoch [77/100], Step [120/138], Loss: 0.0849\n",
      "Epoch [78/100], Step [20/138], Loss: 0.1722\n",
      "Epoch [78/100], Step [40/138], Loss: 0.0614\n",
      "Epoch [78/100], Step [60/138], Loss: 0.0852\n",
      "Epoch [78/100], Step [80/138], Loss: 0.0603\n",
      "Epoch [78/100], Step [100/138], Loss: 0.0798\n",
      "Epoch [78/100], Step [120/138], Loss: 0.0832\n",
      "Epoch [79/100], Step [20/138], Loss: 0.1702\n",
      "Epoch [79/100], Step [40/138], Loss: 0.0602\n",
      "Epoch [79/100], Step [60/138], Loss: 0.0835\n",
      "Epoch [79/100], Step [80/138], Loss: 0.0591\n",
      "Epoch [79/100], Step [100/138], Loss: 0.0782\n",
      "Epoch [79/100], Step [120/138], Loss: 0.0816\n",
      "Epoch [80/100], Step [20/138], Loss: 0.1684\n",
      "Epoch [80/100], Step [40/138], Loss: 0.0589\n",
      "Epoch [80/100], Step [60/138], Loss: 0.0819\n",
      "Epoch [80/100], Step [80/138], Loss: 0.0579\n",
      "Epoch [80/100], Step [100/138], Loss: 0.0768\n",
      "Epoch [80/100], Step [120/138], Loss: 0.0801\n",
      "Epoch [81/100], Step [20/138], Loss: 0.1665\n",
      "Epoch [81/100], Step [40/138], Loss: 0.0577\n",
      "Epoch [81/100], Step [60/138], Loss: 0.0803\n",
      "Epoch [81/100], Step [80/138], Loss: 0.0568\n",
      "Epoch [81/100], Step [100/138], Loss: 0.0753\n",
      "Epoch [81/100], Step [120/138], Loss: 0.0786\n",
      "Epoch [82/100], Step [20/138], Loss: 0.1648\n",
      "Epoch [82/100], Step [40/138], Loss: 0.0566\n",
      "Epoch [82/100], Step [60/138], Loss: 0.0788\n",
      "Epoch [82/100], Step [80/138], Loss: 0.0557\n",
      "Epoch [82/100], Step [100/138], Loss: 0.0740\n",
      "Epoch [82/100], Step [120/138], Loss: 0.0771\n",
      "Epoch [83/100], Step [20/138], Loss: 0.1631\n",
      "Epoch [83/100], Step [40/138], Loss: 0.0555\n",
      "Epoch [83/100], Step [60/138], Loss: 0.0773\n",
      "Epoch [83/100], Step [80/138], Loss: 0.0547\n",
      "Epoch [83/100], Step [100/138], Loss: 0.0726\n",
      "Epoch [83/100], Step [120/138], Loss: 0.0757\n",
      "Epoch [84/100], Step [20/138], Loss: 0.1614\n",
      "Epoch [84/100], Step [40/138], Loss: 0.0544\n",
      "Epoch [84/100], Step [60/138], Loss: 0.0759\n",
      "Epoch [84/100], Step [80/138], Loss: 0.0537\n",
      "Epoch [84/100], Step [100/138], Loss: 0.0713\n",
      "Epoch [84/100], Step [120/138], Loss: 0.0743\n",
      "Epoch [85/100], Step [20/138], Loss: 0.1598\n",
      "Epoch [85/100], Step [40/138], Loss: 0.0534\n",
      "Epoch [85/100], Step [60/138], Loss: 0.0745\n",
      "Epoch [85/100], Step [80/138], Loss: 0.0527\n",
      "Epoch [85/100], Step [100/138], Loss: 0.0701\n",
      "Epoch [85/100], Step [120/138], Loss: 0.0730\n",
      "Epoch [86/100], Step [20/138], Loss: 0.1583\n",
      "Epoch [86/100], Step [40/138], Loss: 0.0524\n",
      "Epoch [86/100], Step [60/138], Loss: 0.0732\n",
      "Epoch [86/100], Step [80/138], Loss: 0.0518\n",
      "Epoch [86/100], Step [100/138], Loss: 0.0689\n",
      "Epoch [86/100], Step [120/138], Loss: 0.0717\n",
      "Epoch [87/100], Step [20/138], Loss: 0.1568\n",
      "Epoch [87/100], Step [40/138], Loss: 0.0515\n",
      "Epoch [87/100], Step [60/138], Loss: 0.0719\n",
      "Epoch [87/100], Step [80/138], Loss: 0.0508\n",
      "Epoch [87/100], Step [100/138], Loss: 0.0677\n",
      "Epoch [87/100], Step [120/138], Loss: 0.0705\n",
      "Epoch [88/100], Step [20/138], Loss: 0.1553\n",
      "Epoch [88/100], Step [40/138], Loss: 0.0505\n",
      "Epoch [88/100], Step [60/138], Loss: 0.0706\n",
      "Epoch [88/100], Step [80/138], Loss: 0.0500\n",
      "Epoch [88/100], Step [100/138], Loss: 0.0666\n",
      "Epoch [88/100], Step [120/138], Loss: 0.0693\n",
      "Epoch [89/100], Step [20/138], Loss: 0.1539\n",
      "Epoch [89/100], Step [40/138], Loss: 0.0496\n",
      "Epoch [89/100], Step [60/138], Loss: 0.0694\n",
      "Epoch [89/100], Step [80/138], Loss: 0.0491\n",
      "Epoch [89/100], Step [100/138], Loss: 0.0655\n",
      "Epoch [89/100], Step [120/138], Loss: 0.0681\n",
      "Epoch [90/100], Step [20/138], Loss: 0.1525\n",
      "Epoch [90/100], Step [40/138], Loss: 0.0488\n",
      "Epoch [90/100], Step [60/138], Loss: 0.0682\n",
      "Epoch [90/100], Step [80/138], Loss: 0.0483\n",
      "Epoch [90/100], Step [100/138], Loss: 0.0644\n",
      "Epoch [90/100], Step [120/138], Loss: 0.0670\n",
      "Epoch [91/100], Step [20/138], Loss: 0.1512\n",
      "Epoch [91/100], Step [40/138], Loss: 0.0479\n",
      "Epoch [91/100], Step [60/138], Loss: 0.0671\n",
      "Epoch [91/100], Step [80/138], Loss: 0.0475\n",
      "Epoch [91/100], Step [100/138], Loss: 0.0634\n",
      "Epoch [91/100], Step [120/138], Loss: 0.0659\n",
      "Epoch [92/100], Step [20/138], Loss: 0.1499\n",
      "Epoch [92/100], Step [40/138], Loss: 0.0471\n",
      "Epoch [92/100], Step [60/138], Loss: 0.0660\n",
      "Epoch [92/100], Step [80/138], Loss: 0.0467\n",
      "Epoch [92/100], Step [100/138], Loss: 0.0624\n",
      "Epoch [92/100], Step [120/138], Loss: 0.0648\n",
      "Epoch [93/100], Step [20/138], Loss: 0.1486\n",
      "Epoch [93/100], Step [40/138], Loss: 0.0463\n",
      "Epoch [93/100], Step [60/138], Loss: 0.0649\n",
      "Epoch [93/100], Step [80/138], Loss: 0.0459\n",
      "Epoch [93/100], Step [100/138], Loss: 0.0614\n",
      "Epoch [93/100], Step [120/138], Loss: 0.0638\n",
      "Epoch [94/100], Step [20/138], Loss: 0.1474\n",
      "Epoch [94/100], Step [40/138], Loss: 0.0455\n",
      "Epoch [94/100], Step [60/138], Loss: 0.0638\n",
      "Epoch [94/100], Step [80/138], Loss: 0.0452\n",
      "Epoch [94/100], Step [100/138], Loss: 0.0604\n",
      "Epoch [94/100], Step [120/138], Loss: 0.0628\n",
      "Epoch [95/100], Step [20/138], Loss: 0.1462\n",
      "Epoch [95/100], Step [40/138], Loss: 0.0448\n",
      "Epoch [95/100], Step [60/138], Loss: 0.0628\n",
      "Epoch [95/100], Step [80/138], Loss: 0.0445\n",
      "Epoch [95/100], Step [100/138], Loss: 0.0595\n",
      "Epoch [95/100], Step [120/138], Loss: 0.0618\n",
      "Epoch [96/100], Step [20/138], Loss: 0.1451\n",
      "Epoch [96/100], Step [40/138], Loss: 0.0441\n",
      "Epoch [96/100], Step [60/138], Loss: 0.0618\n",
      "Epoch [96/100], Step [80/138], Loss: 0.0438\n",
      "Epoch [96/100], Step [100/138], Loss: 0.0586\n",
      "Epoch [96/100], Step [120/138], Loss: 0.0608\n",
      "Epoch [97/100], Step [20/138], Loss: 0.1439\n",
      "Epoch [97/100], Step [40/138], Loss: 0.0434\n",
      "Epoch [97/100], Step [60/138], Loss: 0.0609\n",
      "Epoch [97/100], Step [80/138], Loss: 0.0431\n",
      "Epoch [97/100], Step [100/138], Loss: 0.0578\n",
      "Epoch [97/100], Step [120/138], Loss: 0.0599\n",
      "Epoch [98/100], Step [20/138], Loss: 0.1429\n",
      "Epoch [98/100], Step [40/138], Loss: 0.0427\n",
      "Epoch [98/100], Step [60/138], Loss: 0.0600\n",
      "Epoch [98/100], Step [80/138], Loss: 0.0425\n",
      "Epoch [98/100], Step [100/138], Loss: 0.0569\n",
      "Epoch [98/100], Step [120/138], Loss: 0.0590\n",
      "Epoch [99/100], Step [20/138], Loss: 0.1418\n",
      "Epoch [99/100], Step [40/138], Loss: 0.0420\n",
      "Epoch [99/100], Step [60/138], Loss: 0.0591\n",
      "Epoch [99/100], Step [80/138], Loss: 0.0418\n",
      "Epoch [99/100], Step [100/138], Loss: 0.0561\n",
      "Epoch [99/100], Step [120/138], Loss: 0.0581\n",
      "Epoch [100/100], Step [20/138], Loss: 0.1408\n",
      "Epoch [100/100], Step [40/138], Loss: 0.0414\n",
      "Epoch [100/100], Step [60/138], Loss: 0.0582\n",
      "Epoch [100/100], Step [80/138], Loss: 0.0412\n",
      "Epoch [100/100], Step [100/138], Loss: 0.0553\n",
      "Epoch [100/100], Step [120/138], Loss: 0.0573\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(100):\n",
    "    for i, (inputs, targets) in enumerate(dataloader):\n",
    "        # 前向传播\n",
    "        outputs, _ = model(inputs)\n",
    "\n",
    "        # 计算损失\n",
    "        # 用每个时间步的输出和每个时间步的标签进行比较，并平均损失\n",
    "        loss = criterion(\n",
    "            outputs.view(-1, vocab_size),  # (batch_size*seq_length, vocab_size)\n",
    "            targets.view(-1)  # (batch_size*seq_length)\n",
    "        )\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        # 梯度裁剪防止爆炸\n",
    "        # nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
    "        optimizer.step()\n",
    "\n",
    "        if (i + 1) % 20 == 0:\n",
    "            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'\n",
    "                  .format(epoch + 1, 100, i + 1, len(dataloader), loss.item()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8a4e36b033918def",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T04:53:01.928168Z",
     "start_time": "2025-06-30T04:53:01.921519Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "鹰击长空，鱼翔浅底，万类霜天竞自由。\n",
      "怅寥\n"
     ]
    }
   ],
   "source": [
    "model.eval()\n",
    "\n",
    "def generate_text(content, steps, temperature=0.8):\n",
    "\n",
    "    words = [word for word in content]\n",
    "\n",
    "    hidden = None\n",
    "    for _ in range(steps):\n",
    "        # inputs = [word_to_idx[word] for word in words[-SEQ_LENGTH:]] # 取输入的最后SEQ_LENGTH个词的索引\n",
    "        inputs = [word_to_idx[word] for word in words[-1:]] # 取输入的最后SEQ_LENGTH个词的索引\n",
    "        inputs = torch.LongTensor(inputs)\n",
    "\n",
    "        # 输入形状调整\n",
    "        inputs = inputs.view(1, -1)  # (1, seq_len)\n",
    "\n",
    "        # 前向传播\n",
    "        with torch.no_grad():\n",
    "            # output中包含了每个时间步的输出，推理预测时，只需要取最后一个时间步的输出即可，比如输入“鹰击”，相当于有两个时间步，但是我们只需要第2个时间步的输出，而输出是词汇表中各个词的概率\n",
    "            # 而hidden表示隐藏层，在推理预测时，因为我们会连续预测，外层有一个for循环，所以hidden需要保存，以便下一次循环使用\n",
    "            outputs, hidden = model(inputs, hidden)\n",
    "            last_output = outputs[0, -1, :]  # 取最后一个时间步的输出\n",
    "\n",
    "        # 应用温度采样\n",
    "        # last_output / temperature，相当于将last_output缩小，比如[8,2,2] / 2 = [4,1,1]，使得三个选项对应的数字之间的差别变小了\n",
    "        # 当然如果temperature<1，那么就是放大差别，比如[8,2,2] / 0.5 = [16,4,4]\n",
    "        # probs为做了softmax之后的概率\n",
    "        probs = torch.softmax(last_output / temperature, dim=-1)\n",
    "\n",
    "        # 多项式采样，probs是一个概率，比如是[0.3,0.2,0.5]，那么就是从0,1,2中随机选一个，那么2被选中的概率就是50%\n",
    "        # 谁的概率大，随被采样的概率就大\n",
    "        result_idx = torch.multinomial(probs, 1).item()\n",
    "\n",
    "        # 更新输入序列\n",
    "        words.append(idx_to_word[result_idx])\n",
    "\n",
    "    return ''.join(words)\n",
    "\n",
    "\n",
    "# 20表示预测20次, temperature越大，越随机\n",
    "print(generate_text(\"鹰\", 20, temperature=0.1))"
   ]
  },
  {
   "attachments": {
    "c66ccf40-ad70-4713-8bd3-99fbab47bb43.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "3c7f0fa005fd9716",
   "metadata": {},
   "source": [
    "![image.png](attachment:c66ccf40-ad70-4713-8bd3-99fbab47bb43.png)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
